高光谱激光雷达提取植被生化组分垂直分布
Vertical distribution inversion of biochemical parameters using hyperspectral LiDAR
- 2018年22卷第5期 页码:737-744
纸质出版日期: 2018-9 ,
录用日期: 2017-12-29
DOI: 10.11834/jrs.20187244
扫 描 看 全 文
浏览全部资源
扫码关注微信
纸质出版日期: 2018-9 ,
录用日期: 2017-12-29
扫 描 看 全 文
高帅, 牛铮, 孙刚, 覃驭楚, 李旺, 田海峰. 2018. 高光谱激光雷达提取植被生化组分垂直分布. 遥感学报, 22(5): 737–744
Gao S, Niu Z, Sun G, Qin Y C, Li W and Tian H F. 2018. Vertical distribution inversion of biochemical parameters using hyperspectral LiDAR. Journal of Remote Sensing, 22(5): 737–744
对地高光谱激光雷达可以获得观测对象含有高光谱属性的全波形激光雷达回波,为探测植被生化特征的立体分布提供了新的遥感探测手段。基于此仪器开展室内试验,提出了植被生化组分垂直分布提取方法。首先,针对仪器的特点,提出了高光谱激光雷达全波形数据处理的方法;其次,以火炬花为例开展了室内扫描,并对获取的高光谱激光雷达数据进行了处理,获得带有高光谱属性的激光雷达点云数据;最后,根据植被指数与生化组分的关系,提取了叶绿素和胡萝卜素的生化组分垂直分布结果。研究结果表明,在植被顶部生化组分含量较低,叶绿素a普遍低于0.5 mg/g,胡萝卜素低于0.2 mg/g,而在中部叶片处,生化组分含量明显较高,与红色(顶部)和绿色叶片(中部)在植被垂直方向的分布一致,这表明基于仪器开展植被生理生化参数垂直分布遥感反演具有极大的应用潜力。
A hyperspectral Light Detection And Ranging (LiDAR) can obtain high spectral properties of the observed object and provides a new method for detecting a three dimesional distribution of vegetation structure and biochemical characteristics. In this study
a data process flow and a biochemical characteristics method were proposed. Laboratory experiments were conducted on the basis of this instrument
and a vertical distribution extraction method of vegetation biochemical components was provided. First
we proposed the hyperspectral LiDAR waveform data processing method in accordance with the characteristics of the instrument. Second
an indoor Kniphofia scanning experiment was utilized
and the LiDAR point cloud data with high spectral properties were obtained. Finally
chlorophyll and carotene vertical distributions were extracted on the basis of the relationship between the vegetation index and biochemical components. Results show that the biochemical content of a red leaf at the top of vegetation is low
which is generally lower than 0.5 mg/g
and carotene is less than 0.2 mg/g. However
the biochemical component content in the middle of the green leaves was evidently high. This study showed that the instrument has a considerable application prospect in the field of quantitative remote sensing.
高光谱激光雷达生化组分垂直分布全波形点云分布
hyperspectral LiDARbiochemical parametervertical distributionfull waveformpoint cloud
Asner G P, Anderson C B, Martin R E, Knapp D E, Tupayachi R, Sinca F and Malhi Y. 2014. Landscape-scale changes in forest structure and functional traits along an Andes-to-Amazon elevation gradient. Biogeosciences, 11(3): 843–856
Dalponte M, Ørka H O, Ene L T, Gobakken T and Næsset E. 2014. Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sensing of Environment, 140: 306–317
Gao S, Niu Z, Sun G, Zhao D, Jia K and Qin Y C. 2015. Height extraction of maize using airborne full-waveform LIDAR data and a deconvolution algorithm. IEEE Geoscience and Remote Sensing Letters, 12(9): 1978–1982
Ghosh A, Fassnacht F E, Joshi P K and Koch B. 2014. A framework for mapping tree species combining hyperspectral and LiDAR data: role of selected classifiers and sensor across three spatial scales. International Journal of Applied Earth Observation and Geoinformation, 26: 49–63
Gong W, Song S L, Zhu B, Shi S, Li F Q and Cheng X W. 2012. Multi-wavelength canopy LiDAR for remote sensing of vegetation: design and system performance. ISPRS Journal of Photogrammetry and Remote Sensing, 69: 1–9
Hakala T, Suomalainen J, Kaasalainen S and Chen Y W. 2012. Full waveform hyperspectral LiDAR for terrestrial laser scanning. Optics Express, 20(7): 7119–7127
Hopkinson C, Chasmer L, Gynan C, Mahoney C and Sitar M. 2016. Multisensor and multispectral LiDAR characterization and classification of a forest environment. Canadian Journal of Remote Sensing, 42(5): 501–520
Kulawardhana R W, Popescu S C and Feagin R A. 2014. Fusion of lidar and multispectral data to quantify salt marsh carbon stocks. Remote Sensing of Environment, 154: 345–357
Li W, Sun G, Niu Z, Gao S and Qiao H L. 2014. Estimation of leaf biochemical content using a novel hyperspectral full-waveform LiDAR system. Remote Sensing Letters, 5(8): 693–702
李增元, 刘清旺, 庞勇. 2016. 激光雷达森林参数反演研究进展. 遥感学报, 20(5): 1138–1150
Li Z Y, Liu Q W and Pang Y. 2016. Review on forest parameters inversion using LiDAR. Journal of Remote Sensing, 20(5): 1138–1150 (
刘清旺, 李增元, 陈尔学, 庞勇, 李世明, 田昕. 2011. 森林冠层探测激光雷达的波形特征分析. 中国科学: 地球科学, 41(11): 1670–1678
Liu Q W, Li Z Y, Chen E X, Pang Y, Li S M and Tian X. 2011. Feature analysis of LIDAR waveforms from forest canopies. Science China Earth Sciences, 41(11): 1670–1678 (
刘正军, 梁静, 张继贤. 2014. 空间域分割的机载LiDAR数据输电线快速提取. 遥感学报, 18(1): 61–76
Liu Z J, Liang J and Zhang J X. 2014. Power lines extraction from airborne LiDAR data using spatial domain segmentation. Journal of Remote Sensing, 18(1): 61–76 (
Mallet C and Bretar F. 2009. Full-waveform topographic lidar: state-of-the-art. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1): 1–16
Morsdorf F, Nichol C, Malthus T and Woodhouse I H. 2009. Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling. Remote Sensing of Environment, 113(10): 2152–2163
Niu Z, Xu Z G, Sun G, Huang W J, Wang L, Feng M B, Li W, He W B and Gao S. 2015. Design of a new multispectral waveform LiDAR instrument to monitor vegetation. IEEE Geoscience and Remote Sensing Letters, 12(7): 1506–1510
庞勇, 李增元, 陈尔学, 孙国清. 2005. 激光雷达技术及其在林业上的应用. 林业科学, 41(3): 129–136
Pang Y, Li Z Y, Chen E X and Sun G Q. 2005. Lidar remote sensing technology and its application in forestry. Scientia Silvae Sinicae, 41(3): 129–136 (
Rall J A R and Knox R G. 2004. Spectral ratio biospheric lidar//Proceedings of IEEE International Geoscience and Remote Sensing Symposium. Anchorage, AK: IEEE, 3: 1951–1954 [DOI: 10.1109/IGARSS.2004.1370726]
Sun G, Niu Z, Gao S, Huang W J, Wang L, Li W and Feng M B. 2014. 32-channel hyperspectral waveform LiDAR instrument to monitor vegetation: design and initial performance trials//Proceedings of the SPIE Volume 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V. Beijing, China: SPIE, 9263: 926331 [DOI: 10.1117/12.2066788]
Suomalainen J, Hakala T, Kaartinen H, Räikkönen E and Kaasalainen S. 2011. Demonstration of a virtual active hyperspectral LiDAR in automated point cloud classification. ISPRS Journal of Photogrammetry and Remote Sensing, 66(5): 637–641
王纪华, 王之杰, 黄文江, 马智宏, 刘良云, 赵春江. 2004. 冬小麦冠层氮素的垂直分布及光谱响应. 遥感学报, 8(4): 309–316
Wang J H, Wang Z J, Huang W J, Ma Z H, Liu L Y and Zhao C J. 2004. The vertical distribution characteristic and spectral response of canopy nitrogen in different layer of winter wheat. Journal of Remote Sensing, 8(4): 309–316 (
Wing B M, Ritchie M W, Boston K, Cohen W B, Gitelman A and Olsen M J. 2012. Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest. Remote Sensing of Environment, 124: 730–741
阎广建, 吴均, 王锦地, 朱重光, 李小文. 2002. 光谱先验知识在植被结构遥感反演中的应用. 遥感学报, 6(1): 1–6
Yan G J, Wu J, Wang J D, Zhu C G and Li X W. 2002. Spectral prior knowledge and its use in the remote sensing based inversion of vegetation structure. Journal of Remote Sensing, 6(1): 1–6 (
Yang T, Wang C, Li G C, Luo S Z, Xi X H, Gao S and Zeng H C. 2015. Forest canopy height mapping over China using GLAS and MODIS data. Science China Earth Sciences, 58(1): 96–105
赵春江, 黄文江, 王纪华, 刘良云, 宋晓宇, 马智宏, 李存军. 2006. 用多角度光谱信息反演冬小麦叶绿素含量垂直分布. 农业工程学报, 22(6): 104–109
Zhao C J, Huang W J, Wang J H, Liu L Y, Song X Y, Ma Z H and Li C J. 2006. Extracting winter wheat chlorophyll concentration vertical distribution based on bidirectional canopy reflected spectrum. Transactions of the CSAE, 22(6): 104–109 (
相关作者
相关机构